The present invention generally relates to the analysis of ratiometric data, e.g., ratiometric image data such as fluorescent image data.
Ratiometric imaging, for example, may include the acquisition of fluorescent images at two different wavelengths. At one wavelength, for example, a change or changes in a variable of interest (e.g., composition or object being imaged) may cause a fractional change in the fluorescence intensity, while at the other wavelength, the fluorescence intensity may either change opposite to the change in the first wavelength, or the fluorescence may remain unchanged. In other words, the signals are either anti-correlated or divergent. Problems can arise when the ratiometric image series are noisy and/or the signals are weak. For example, noise may cause the denominator in the ratio (e.g., of the ratiometric data) to become excessively small, making the ratio to be excessively (and spuriously) large.
A method of analyzing a single ratiometric dataset using singular value decomposition to find covarying information has previously been investigated (see, e.g., J. Broder, A. Majumder, C. H. Keith, J. D. Lauderdale and A. Sornborger, “Multivariate methods for the analysis of multichannel NADH/Flavoprotein and ratiometric calcium imaging signals,” Poster: Program No. 457.9, 2005 Abstract Viewer/Itinerary Planner, Washington, D.C., Society for Neuroscience).
Methods of finding correlated information across multiple datasets have previously been investigated. For example, Hotelling investigated finding correlated information across two datasets (see, e.g., 2. H. Hotelling, “Relations between two sets of variates,” Biometrika 28:321-377 (1936)). Also, for example, Carroll investigated finding correlated information across multiple datasets (see, e.g., J. D. Carroll, “Generalization of canonical correlation analysis to three or more sets of variables,” Proc. 76th Ann. Conv. APA pp. 227-228 (1968)).